In Direct Sequence Code Division Multiple Access (DS-CDMA) systems, such as High Speed Packet Access (HSPA) services in Wideband CDMA (WCDMA) and similar packet services in 1 XEV-DO, different symbols are transmitted together through a mixture of code-division multiplexing (CDM) and time-division multiplexing (TDM). For example, downlink transmissions use orthogonal spreading codes to send a block of symbols for one or more users at a time. Symbol blocks are sent sequentially in orthogonal symbol periods. Uplink transmissions from the terminals to the network use a similar approach, sometimes referred to as multicode transmission, to send blocks of symbols for one user. Sometimes, as in the case of HSPA, symbol blocks use a mixture of different spreading factors, while still maintaining orthogonal transmission.
In non-dispersive environments, orthogonality between symbols within a block and between different symbol-period blocks can be preserved through accurate synchronization and filtering. However, transmission channels in wireless communication networks are often dispersive, which destroys orthogonality. The loss of orthogonality between codes creates intersymbol interference (ISI), both between time-successive symbol blocks and between the symbols within each symbol block. In other words, with dispersive transmission channels, a symbol within any given symbol block in a time-wise stream of symbol blocks suffers interference arising from other symbols in the same block, and interference arising from other symbol blocks.
As transmission data rates become higher, such as in HSPA, processing gain is not sufficient to compensate for ISI. For example, for peak uplink rates of 11 Mbps or greater, there is a gap in performance between linear equalization and the matched filter bound. That gap indicates that meaningful receiver performance gains could be realized with more advanced forms of interference suppression. Of course, the challenges associated with implementing advanced interference suppression involve a range of design and operational tradeoffs between performance and complexity.
Several known solutions address ISI through some form of equalization. A first known approach is referred to as linear equalization (LE), wherein linear filtering (addition and multiplication operations) is applied to the received signal, to suppress ISI for recovery of each symbol of interest. Conventional LE filtering uses filter coefficients calculated to suppress inter-block interference (interference between sequential symbol blocks), as well as intra-block interference (interference between symbols within a block). This known approach generally is paired with simple, single-symbol detection that is applied to LE filter outputs, for recovery of individual symbols within each block. Such an approach trades symbol energy for ISI reduction.
Another known solution, known as decision feedback equalization (DFE), applies a combination of linear filtering and interference subtraction to the received signal, to recover each symbol of interest. With DFE, a subtractive process suppresses inter-block interference from previously detected blocks, while linear filtering suppresses inter-block interference from future, undetected blocks. Intra-block interference between the symbols within one block is handled as either part of the linear filtering or through a combination of linear filtering and sequential detection and subtraction (decorrelating decision feedback equalizer or DDFE). Like linear equalization, this approach trades signal energy for ISI reduction, but potentially less symbol energy is traded, as the linear filter does not necessarily need to suppress past inter-block interference. As with linear equalization, simple single-symbol detection generally is used to detect the individual symbols in each symbol block being processed. An exception is the block DFE described in Williamson et al., “Block Decision Feedback Equalization,” IEEE Transactions on Communications, February 1992, where joint detection is applied to blocks of symbols. However, there is no feedforward filtering, so that only partial signal energy is used in detecting symbols.
Another known approach improves over LE and DFE because it does not trade symbol energy for ISI reduction. Such an approach, using maximum likelihood sequence estimation (MLSE) or Maximum A Posteriori (MAP) symbol detection, forms and accumulates metrics which are then used for jointly detecting sequences of symbols. Such detection addresses both inter-block and intra-block interference, but it comes at the expense of significantly more complex processing because of the need to hypothesize many symbol combinations, and to maintain correspondingly complex state spaces and accumulation metrics.